While some forms of social coordination appear in tools such as Meetup and in
game platforms such as XBox LIVE, we introduce a more general model using what we
call enmeshed queries. An enmeshed query allows users to declaratively specify an
intent to coordinate with other users (who they may not know a priori) by
specifying constraints on who/what/when as well as on the composition of the
group, such as the desired group size. The database returns a group of users who
have registered queries with matching intents. Enmeshed queries are continuous,
but new queries (and not data) answer older queries; the group constraints and
the ability to coordinate with unknown partners make enmeshed queries different
from entangled queries, publish-subscribe systems, dating services and nested
transactions. While even onliine group coordination using enmeshed queries is
NP-hard, we introduce effifficient heuristic algorithms that can scale to
millions of queries, and find 86{\%} of the matches found by an optimal algorithm
using 40 microseconds per query on a 2.5 GHz server machine. We conclude by
describing potential generalizations that add prices, recommendations, and data
mining to basic enmeshed queries.